Logistic regression is the foundation of a large number of parameterized models [1]}, [2]}.
In the early 1990s, the degree of effective freedom is proposed as a complexity measure for linear models and penalized linear models, which is represented by Vapnik-Chervoneniks (VC) dimension [3]}. Besides, Rademacher complexity and Gaussian complexity [4]}, [5]} are also used to measure model complexity of logistic regression models [1]}. Comparing to VC dimension, Rademacher complexity takes data distribution into consideration and therefore reflects finer-grained model complexity.
Later, Bulso et al. [2]} and Balasubramanian [8]} suggest that model complexity of logistic regression models is related to the number of distinguishable distributions that can be represented by the models. Bulso et al. [2]} define a complexity measure of logistic regression models based on the determinant of the Fisher Information matrix.
